update configuration files

add supervise_type

Author: taigw

README.md

@@ -15,7 +15,7 @@ python setup.py install
 ``` 
 
 ## Data
-The datasets for the examples can be downloaded from [Google Drive][google_link] or [Baidu Disk][baidu_link] (extraction code: n07g). Extract the files to `PyMIC_data` after the download. 
+The datasets for the examples can be downloaded from [Google Drive][google_link] or [Baidu Disk][baidu_link] (extraction code: xlwg). Extract the files to `PyMIC_data` after downloading. 
 
 
 ## List of Examples
@@ -35,8 +35,8 @@ Currently we provide the following examples in this repository:
 |Noisy label learning|[seg_nll/JSRT][nll_jsrt_link]|Comparing different NLL methods for learning from noisy labels|
 
 [PyMIC_link]: https://github.com/HiLab-git/PyMIC
-[google_link]:https://drive.google.com/file/d/1-LrMHsX7ZdBto2iC1WnbFFZ0tDeJQFHy/view?usp=sharing
-[baidu_link]:https://pan.baidu.com/s/15mjc0QqH75xztmc23PPWQQ 
+[google_link]:https://drive.google.com/file/d/1eZakSEBr_zfIHFTAc96OFJix8cUBf-KR/view?usp=sharing
+[baidu_link]:https://pan.baidu.com/s/1tN0inIrVYtSxTVRfErD9Bw 
 [AntBee_link]:classification/AntBee
 [CHNCXR_link]:classification/CHNCXR
 [JSRT_link]:segmentation/JSRT

seg_nll/JSRT/README.md

@@ -39,7 +39,10 @@ The dataset setting is similar to that in the `segmentation/JSRT` demo. See `con
 
 ```bash
 ...
-task_type = seg
+tensor_type    = float
+task_type      = seg
+supervise_type = fully_sup
+
 root_dir  = ../../PyMIC_data/JSRT
 train_csv = config/data/jsrt_train_mix.csv
 valid_csv = config/data/jsrt_valid.csv
@@ -51,8 +54,8 @@ loss_type     = CrossEntropyLoss
 The following commands are used for training and inference with this method, respectively:
 
 ```bash
-pymic_run train config/unet_ce.cfg
-pymic_run test config/unet_ce.cfg
+pymic_train config/unet_ce.cfg
+pymic_test config/unet_ce.cfg
 ```
 
 ### GCE Loss
@@ -67,8 +70,8 @@ loss_type     = GeneralizedCELoss
 The following commands are used for training and inference with this method, respectively:
 
 ```bash
-pymic_run train config/unet_gce.cfg
-pymic_run test config/unet_gce.cfg
+pymic_train config/unet_gce.cfg
+pymic_test config/unet_gce.cfg
 ```
 
 ### CLSLSR
@@ -81,33 +84,45 @@ python clslsr_get_condience config/unet_ce.cfg
 The weight maps will be saved in `$root_dir/slsr_conf`. Then train the new model and do inference by:
 
 ```bash
-pymic_run train config/unet_clslsr.cfg
-pymic_run test config/unet_clslsr.cfg
+pymic_train config/unet_clslsr.cfg
+pymic_test config/unet_clslsr.cfg
 ```
 
 Note that the weight maps for training images are specified in the configuration file `train_csv = config/data/jsrt_train_mix_clslsr.csv`.
 
 ### Co-Teaching
-The configuration file for Co-Teaching is `config/unet2d_cot.cfg`. The corresponding setting is:
+The configuration file for Co-Teaching is `config/unet2d_cot.cfg`. Note that for the following methods, `supervise_type` should be set to  `noisy_label`.
 
 ```bash
-nll_method   = CoTeaching
+[dataset]
+...
+supervise_type = noisy_label
+...
+
+[noisy_label_learning]
+method_name  = CoTeaching
 co_teaching_select_ratio  = 0.8  
 rampup_start = 1000
 rampup_end   = 8000
 ```
 
 The following commands are used for training and inference with this method, respectively:
 ```bash
-pymic_nll train config/unet_cot.cfg
-pymic_nll test config/unet_cot.cfg
+pymic_train config/unet_cot.cfg
+pymic_test config/unet_cot.cfg
 ```
 
 ### TriNet
 The configuration file for TriNet is `config/unet_trinet.cfg`. The corresponding setting is:
 
 ```bash 
-nll_method   = TriNet
+[dataset]
+...
+supervise_type = noisy_label
+...
+
+[noisy_label_learning]
+method_name  = TriNet
 trinet_select_ratio = 0.9
 rampup_start = 1000
 rampup_end   = 8000
@@ -116,15 +131,21 @@ rampup_end   = 8000
 The following commands are used for training and inference with this method, respectively:
 
 ```bash
-pymic_nll train config/unet_trinet.cfg
-pymic_nll test config/unet_trinet.cfg
+pymic_train config/unet_trinet.cfg
+pymic_test config/unet_trinet.cfg
 ```
 
 ### DAST
 The configuration file for DAST is `config/unet_dast.cfg`. The corresponding setting is:
 
 ```bash
-nll_method   = DAST
+[dataset]
+...
+supervise_type = noisy_label
+...
+
+[noisy_label_learning]
+method_name  = DAST
 dast_dbc_w   = 0.1
 dast_st_w    = 0.1  
 dast_rank_length  = 20
@@ -136,8 +157,8 @@ rampup_end   = 8000
 The commands for training and inference are:
 
 ```bash
-pymic_nll train config/unet_dast.cfg
-pymic_run test config/unet_dast.cfg
+pymic_train config/unet_dast.cfg
+pymic_test config/unet_dast.cfg
 ```
 
 ## Evaluation

seg_nll/JSRT/config/unet_ce.cfg

@@ -1,8 +1,9 @@
 [dataset]
 # tensor type (float or double)
-tensor_type = float
+tensor_type    = float
+task_type      = seg
+supervise_type = fully_sup
 
-task_type = seg
 root_dir  = ../../PyMIC_data/JSRT
 train_csv = config/data/jsrt_train_mix.csv
 valid_csv = config/data/jsrt_valid.csv
@@ -64,8 +65,6 @@ ReduceLROnPlateau_patience = 2000
 ckpt_save_dir    = model/unet_ce
 ckpt_prefix      = unet_ce
 
-# start iter
-iter_start = 0
 iter_max   = 10000
 iter_valid = 100
 iter_save  = [10000]

seg_nll/JSRT/config/unet_clslsr.cfg

@@ -1,8 +1,9 @@
 [dataset]
 # tensor type (float or double)
-tensor_type = float
+tensor_type    = float
+task_type      = seg
+supervise_type = fully_sup
 
-task_type = seg
 root_dir  = ../../PyMIC_data/JSRT
 train_csv = config/data/jsrt_train_mix_clslsr.csv
 valid_csv = config/data/jsrt_valid.csv
@@ -65,8 +66,6 @@ early_stop_patience = 4000
 ckpt_save_dir    = model/unet_clslsr
 ckpt_prefix      = unet_clslsr
 
-# start iter
-iter_start = 0
 iter_max   = 10000
 iter_valid = 100
 iter_save  = [10000]

seg_nll/JSRT/config/unet_cot.cfg

@@ -1,8 +1,9 @@
 [dataset]
 # tensor type (float or double)
-tensor_type = float
+tensor_type    = float
+task_type      = seg
+supervise_type = noisy_label 
 
-task_type = seg
 root_dir  = ../../PyMIC_data/JSRT
 train_csv = config/data/jsrt_train_mix.csv
 valid_csv = config/data/jsrt_valid.csv
@@ -68,7 +69,7 @@ iter_valid = 100
 iter_save  = [10000]
 
 [noisy_label_learning]
-nll_method   = CoTeaching
+method_name  = CoTeaching
 co_teaching_select_ratio  = 0.8  
 rampup_start = 1000
 rampup_end   = 8000

seg_nll/JSRT/config/unet_dast.cfg

@@ -1,8 +1,9 @@
 [dataset]
 # tensor type (float or double)
-tensor_type = float
+tensor_type    = float
+task_type      = seg
+supervise_type = noisy_label
 
-task_type = seg
 root_dir  = ../../PyMIC_data/JSRT
 train_csv = config/data/jsrt_train_clean.csv
 train_csv_noise = config/data/jsrt_train_noise.csv
@@ -70,7 +71,7 @@ iter_valid = 100
 iter_save  = [10000]
 
 [noisy_label_learning]
-nll_method   = DAST
+method_name  = DAST
 dast_dbc_w   = 0.1
 dast_st_w    = 0.1  
 dast_rank_length  = 20

seg_nll/JSRT/config/unet_gce.cfg

@@ -1,8 +1,9 @@
 [dataset]
 # tensor type (float or double)
-tensor_type = float
+tensor_type    = float
+task_type      = seg
+supervise_type = fully_sup
 
-task_type = seg
 root_dir  = ../../PyMIC_data/JSRT
 train_csv = config/data/jsrt_train_mix.csv
 valid_csv = config/data/jsrt_valid.csv
@@ -64,8 +65,6 @@ ReduceLROnPlateau_patience = 2000
 ckpt_save_dir    = model/unet_gce
 ckpt_prefix      = unet_gce
 
-# start iter
-iter_start = 0
 iter_max   = 10000
 iter_valid = 100
 iter_save  = [10000]

seg_nll/JSRT/config/unet_trinet.cfg

@@ -1,8 +1,9 @@
 [dataset]
 # tensor type (float or double)
-tensor_type = float
+tensor_type    = float
+task_type      = seg
+supervise_type = noisy_label
 
-task_type = seg
 root_dir  = ../../PyMIC_data/JSRT
 train_csv = config/data/jsrt_train_mix.csv
 valid_csv = config/data/jsrt_valid.csv
@@ -64,14 +65,12 @@ ReduceLROnPlateau_patience = 2000
 ckpt_save_dir    = model/unet_trinet
 ckpt_prefix      = trinet
 
-# start iter
-iter_start = 0
 iter_max   = 10000
 iter_valid = 100
 iter_save  = [10000]
 
 [noisy_label_learning]
-nll_method   = TriNet
+method_name   = TriNet
 trinet_select_ratio = 0.9
 rampup_start = 1000
 rampup_end   = 8000